摘要
针对轨道交通轨旁设备隐蔽螺栓松动、脱落及侵限隐患的检测难题,提出了一种融合图像识别与激光扫描技术的智能巡检系统。该系统以双轨式自走行机器人为载体,集成高清相机与激光雷达,能够在轨道交通隧道桥梁实现自主巡航,通过改进的YOLO-Y算法实现小目标螺栓缺陷的精准识别,并创新性引入动态非单调聚焦机制(Wise-IoU)与Slide Loss分段加权策略,显著提升了低质量图像下的检测鲁棒性,实现了基于图像的轨旁设备固定螺栓松动、脱落缺陷的精确检测;同时设计了轨旁设备侵限检测方案,通过将激光点云三维建模与ICP拼接算法结合,构建了轨旁设备毫米级精度空间模型,提出了基于位移偏差与向量拓扑关系的侵限预警方法。现场测试表明,该系统在杭海城际铁路中缺陷识别准确率达98.08%,可有效实现轨旁设备状态的“事前控制”,为轨道交通智慧运维提供了创新解决方案。
Aiming at the detection challenges of hidden bolt loosening,falling-off,and clearance intrusion risks in rail transit trackside equipment,this paper proposes an intelligent inspection system integrating image recognition and laser scanning technologies.Based on a dual-rail self-propelled robot,the system integrates high-definition cameras and lidar,enabling autonomous cruising in rail transit tunnels and bridges.It achieves accurate identification of small-target bolt defects through the improved YOLO-Y algorithm,and innovatively introduces a dynamic non-monotonic focusing mechanism(Wise-IoU)and a Slide Loss segmented weighting strategy,which significantly enhances detection robustness under low-quality images and realizes precise imagebased detection of loosening and falling-off defects in trackside equipment fixing bolts.Meanwhile,the system incorporates a trackside equipment clearance intrusion detection scheme and related devices.By combining 3D modeling of laser point clouds with the ICP registration algorithm,it constructs a millimeter-level precision spatial model of trackside equipment and proposes a clearance intrusion warning method based on displacement deviation and vector topological relations.Field tests show that the system achieves a defect recognition accuracy of 98.08%in the Hangzhou-Haining Intercity Railway,effectively enabling“pre-event control”of trackside equipment status and providing an innovative solution for smart operation and maintenance of rail transit.
作者
王儒
申路
WANG Ru;SHEN Lu(Zhejiang Haining Rail Transit Operation Management Co.,Ltd.,Haining 314412,China)
出处
《铁道车辆》
2026年第1期21-27,137,共8页
Rolling Stock
基金
浙江省交通运输厅2024年科技计划项目(2024039)。
关键词
轨道交通
轨旁设备
限界检测
机器视觉
激光扫描
rail transit
trackside equipment
clearance gauge inspection
machine vision
laser scanning
